Systems and methods for causing a vehicle response based on traffic light detection

ABSTRACT

A traffic light detection system for a vehicle is provided. The system may include at least one processing device programmed to receive, from at least one image capture device, a plurality of images representative of an area forward of the vehicle, the area including a traffic light fixture having at least one traffic light. The at least one processing device may also be programmed to analyze at least one of the plurality of images to determine a status of the at least one traffic light, and determine an estimated amount of time until the vehicle will reach an intersection associated with the traffic light fixture. The at least one processing device may further be programmed to cause a system response based on the status of at least one traffic light and the estimated amount of time until the vehicle will reach the intersection.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/153,553, filed on Apr. 28, 2015, the contentsof which are incorporated herein by reference in their entirety.

BACKGROUND Technical Field

The present disclosure relates generally to systems and methods forimage processing, and more particularly, to systems and methods forcausing a system response in a vehicle based on traffic light detectionfrom images.

Background Information

As technology continues to advance, more and more vehicles are equippedwith sensors and driving assistance systems configured to assist thedriver in safe driving. Such driving assistance systems may use camerasto capture images of the road environment surrounding the vehicle. Thedriving assistance systems analyze the images to identify pedestrians,road signs, traffic lights, etc., and provide warnings to the drivers.As another example, fully autonomous vehicles are being developed thatcan automatically navigate on the roads without driver input. Autonomousvehicles may take into account a variety of factors and make appropriatedecisions based on those factors to safely and accurately reach anintended destination. For example, an autonomous vehicle may need toprocess and interpret visual information (e.g., images captured by acamera) and may also use information obtained from other sources (e.g.,from a GPS device, a speed sensor, an accelerometer, a suspensionsensor, etc.).

When a conventional or an autonomous vehicle approaches a trafficintersection, the driver or the vehicle itself may need to make adecision as to whether the vehicle should stop or whether the vehicleshould proceed to safely past the traffic intersection. Sometimesdrivers do not pay close attention to the status of traffic lights aheadof the vehicle. In the case of autonomous vehicle, it is still achallenging task for the autonomous vehicle to accurately determinewhether it would be safe to pass an intersection based on informationcollected from various sensors. Therefore, it is desirable and a moderntrend to equip vehicles (whether conventional or autonomous) with asystem that can detect the status of the traffic light and make anappropriate and timely decision on what actions the vehicles or driversshould take in order to avoid collision at the intersection.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle. The disclosed systems may provide a systemresponse based on, for example, an analysis of images captured by one ormore of the cameras. The system response may also take into accountother data including, for example, global positioning system (GPS) data,sensor data (e.g., from an accelerometer, a speed sensor, a suspensionsensor, etc.), and/or other map data. The system response may include awarning to a driver, or other navigational responses, such asacceleration, deceleration, lane shift, maintaining current speed, etc.

Consistent with one embodiment, a traffic light detection system for avehicle is provided. The system may include at least one processingdevice programmed to receive, from at least one image capture device, aplurality of images representative of an area forward of the vehicle,the area including a traffic light fixture having at least one trafficlight. The at least one processing device may also be programmed toanalyze at least one of the plurality of images to determine a status ofthe at least one traffic light, and determine an estimated amount oftime until the vehicle will reach an intersection associated with thetraffic light fixture. The at least one processing device may further beprogrammed to cause a system response based on the status of at leastone traffic light and the estimated amount of time until the vehiclewill reach the intersection.

Consistent with another embodiment, a traffic light detection system fora vehicle is provided. The system may include at least one processingdevice programmed to receive, from at least one image capture device, aplurality of images representative of an area forward of the vehicle,the area including a traffic light fixture having at least one trafficlight. The at least one processing device may also be programmed analyzeat least one of the plurality of images to determine a status of the atleast one traffic light. The at least one processing device may also beprogrammed to determine a distance from the vehicle to an intersection.The at least one processing device may further be programmed to cause asystem response based on the status of at least one traffic light andthe distance from the vehicle to the intersection.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 is an exemplary functional block diagram of a memory configuredto store instructions for performing one or more operations consistentwith the disclosed embodiments.

FIG. 9 illustrates examples of traffic light detection consistent withthe disclosed embodiments.

FIG. 10 illustrates a further example of traffic light detectionconsistent with the disclosed embodiments.

FIG. 11 provides an annotated view of the situation depicted in FIG. 10.

FIG. 12 is a flowchart illustrating a method of detecting a trafficlight, consistent with the disclosed embodiments.

FIG. 13 is a flowchart illustrating another method of detecting atraffic light, consistent with the disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration of the vehicle. To beautonomous, a vehicle need not be fully automatic (e.g., fullyoperational without a driver or without driver input). Rather, anautonomous vehicle includes those that can operate under driver controlduring certain time periods and without driver control during other timeperiods. Autonomous vehicles may also include vehicles that control onlysome aspects of vehicle navigation, such as steering (e.g., to maintaina vehicle course between vehicle lane constraints), but may leave otheraspects to the driver (e.g., braking). In some cases, autonomousvehicles may handle some or all aspects of braking, speed control,and/or steering of the vehicle. In some cases, autonomous vehicles maybe classified into different levels representing the degree ofautomation. For example, the United States National Highway TrafficSafety Administration classifies the autonomous vehicles into fourlevels: level 0 being vehicles without automation; level 1 beingvehicles with function-specific automation; level 2 being vehicles withcombined function automation; level 3 being vehicles with limitedself-driving automation; and level 4 being vehicles with fullself-driving automation.

As human drivers typically rely on visual cues and observations order tocontrol a vehicle, transportation infrastructures are built accordingly,with lane markings, traffic signs, and traffic lights are all designedto provide visual information to drivers. In view of these designcharacteristics of transportation infrastructures, an autonomous vehiclemay include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, components of the transportationinfrastructure (e.g., lane markings, traffic signs, traffic lights,etc.) that are observable by drivers and other obstacles (e.g., othervehicles, pedestrians, debris, etc.). Additionally, an autonomousvehicle may also use stored information, such as information thatprovides a model of the vehicle's environment when navigating. Forexample, the vehicle may use GPS data, sensor data (e.g., from anaccelerometer, a speed sensor, a suspension sensor, etc.), and/or othermap data to provide information related to its environment while it istraveling, and the vehicle (as well as other vehicles) may use theinformation to localize itself on the model.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. In some embodiments,image acquisition unit 120 may include one or more image capture devices(e.g., cameras), such as image capture device 122, image capture device124, and image capture device 126. System 100 may also include a datainterface 128 communicatively connecting processing device 110 to imageacquisition device 120. For example, data interface 128 may include anywired and/or wireless link or links for transmitting image data acquiredby image accusation device 120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), graphicsprocessors, a central processing unit (CPU), support circuits, digitalsignal processors, integrated circuits, memory, or any other types ofdevices suitable for running applications and for image processing andanalysis. In some embodiments, applications processor 180 and/or imageprocessor 190 may include any type of single or multi-core processor,mobile device microcontroller, central processing unit, etc. Variousprocessing devices may be used, including, for example, processorsavailable from manufacturers such as Intel®, AMD®, etc. and may includevarious architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. In other embodiments, configuring a processing device mayinclude storing executable instructions on a memory that is accessibleto the processing device during operation. For example, the processingdevice may access the memory to obtain and execute the storedinstructions during operation.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The support circuits maybe any number of circuits generally well known in the art, includingcache, power supply, clock and input-output circuits. The memory maystore software that, when executed by the processor, controls theoperation of the system. The memory may include databases and imageprocessing software. The memory may comprise any number of random accessmemories, read only memories, flash memories, disk drives, opticalstorage, tape storage, removable storage and other types of storage. Inone instance, the memory may be separate from the processing unit 110.In another instance, the memory may be integrated into the processingunit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware. The memory units may include random access memory, read onlymemory, flash memory, disk drives, optical storage, tape storage,removable storage and/or any other types of storage. In someembodiments, memory units 140, 150 may be separate from the applicationsprocessor 180 and/or image processor 190. In other embodiments, thesememory units may be integrated into applications processor 180 and/orimage processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a tachometer) for measuring a speed of vehicle 200 and/oran accelerometer for measuring acceleration of vehicle 200.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.).

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive dataover one or more networks (e.g., cellular networks, the Internet, etc.).For example, wireless transceiver 172 may upload data collected bysystem 100 to one or more servers, and download data from the one ormore servers. Via wireless transceiver 172, system 100 may receive, forexample, periodic or on demand updates to data stored in map database160, memory 140, and/or memory 150. Similarly, wireless transceiver 172may upload any data (e.g., images captured by image acquisition unit120, data received by position sensor 130 or other sensors, vehiclecontrol systems, etc.) from by system 100 and/or any data processed byprocessing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under setting a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead of transmitdata, such as captured images and/or limited location informationrelated to a route.

Other privacy levels are contemplated. For example, system 100 maytransmit data to a server according to an “intermediate” privacy leveland include additional information not included under a “high” privacylevel, such as a make and/or model of a vehicle and/or a vehicle type(e.g., a passenger vehicle, sport utility vehicle, truck, etc.). In someembodiments, system 100 may upload data according to a “low” privacylevel. Under a “low” privacy level setting, system 100 may upload dataand include information sufficient to uniquely identify a specificvehicle, owner/driver, and/or a portion or entirely of a route traveledby the vehicle. Such “low” privacy level data may include one or moreof, for example, a VIN, a driver/owner name, an origination point of avehicle prior to departure, an intended destination of the vehicle, amake and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV. In some embodiments, imagecapture device 122 may be a 7.2M pixel image capture device with anaspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100degree horizontal FOV. Such an image capture device may be used in placeof a three image capture device configuration. Due to significant lensdistortion, the vertical FOV of such an image capture device may besignificantly less than 50 degrees in implementations in which the imagecapture device uses a radially symmetric lens. For example, such a lensmay not be radially symmetric which would allow for a vertical FOVgreater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e.g.,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5Mpixel, 7M pixel, 10M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope. In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. The disclosed embodiments are not limited to anyparticular configuration of image capture devices 122, 124, and 126,camera mount 370, and glare shield 380. FIG. 3C is an illustration ofcamera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122-126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122-126 may be positioned behind rearviewmirror 310 and positioned substantially side-by-side (e.g., 6 cm apart).Further, in some embodiments, as discussed above, one or more of imagecapture devices 122-126 may be mounted behind glare shield 380 that isflush with the windshield of vehicle 200. Such shielding may act tominimize the impact of any reflections from inside the car on imagecapture devices 122-126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122-126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402-408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto application processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar) to perform the monocular image analysis. As described inconnection with FIGS. 5A-5D below, monocular image analysis module 402may include instructions for detecting a set of features within the setof images, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, and any otherfeature associated with an environment of a vehicle. Based on theanalysis, system 100 (e.g., via processing unit 110) may cause one ormore navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappearing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Traffic lights may use a lamp, an LCD or LED display orlight emitting device, or other lighting technology. Processing unit 110may execute monocular image analysis module 402 to implement process500D. At step 560, processing unit 110 may scan the set of images andidentify objects appearing at locations in the images likely to containtraffic lights. For example, processing unit 110 may filter theidentified objects to construct a set of candidate objects, excludingthose objects unlikely to correspond to traffic lights. The filteringmay be done based on various properties associated with traffic lights,such as shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Such properties may be based on multiple examples oftraffic lights and traffic control signals and stored in a database. Insome embodiments, processing unit 110 may perform multi-frame analysison the set of candidate objects reflecting possible traffic lights. Forexample, processing unit 110 may track the candidate objects acrossconsecutive image frames, estimate the real-world position of thecandidate objects, and filter out those objects that are moving (whichare unlikely to be traffic lights). In some embodiments, processing unit110 may perform color analysis on the candidate objects and identify therelative position of the detected colors appearing inside possibletraffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560-564, processing unit 110 mayidentify traffic lights appearing within the set of captured images andanalyze junction geometry information. Based on the identification andthe analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x₁, z₁)) based on the updated vehicle pathconstructed at step 572. Processing unit 110 may extract the look-aheadpoint from the cumulative distance vector S, and the look-ahead pointmay be associated with a look-ahead distance and look-ahead time. Thelook-ahead distance, which may have a lower bound ranging from 10 to 20meters, may be calculated as the product of the speed of vehicle 200 andthe look-ahead time. For example, as the speed of vehicle 200 decreases,the look-ahead distance may also decrease (e.g., until it reaches thelower bound). The look-ahead time, which may range from 0.5 to 1.5seconds, may be inversely proportional to the gain of one or morecontrol loops associated with causing a navigational response in vehicle200, such as the heading error tracking control loop. For example, thegain of the heading error tracking control loop may depend on thebandwidth of a yaw rate loop, a steering actuator loop, car lateraldynamics, and the like. Thus, the higher the gain of the heading errortracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x₁/z₁). Processingunit 110 may determine the yaw rate command as the product of theheading error and a high-level control gain. The high-level control gainmay be equal to: (2/look-ahead time), if the look-ahead distance is notat the lower bound. Otherwise, the high-level control gain may be equalto: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

System Response Based on Traffic Light Detection

FIG. 8 is an exemplary functional block diagram of memory 140 and/or150, which may be stored or programmed with instructions for performingone or more operations consistent with the disclosed embodiments.Although the following refers to memory 140, one of skill in the artwill recognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 8, memory 140 may store an image analysis module 805.Image analysis module 805 may include a traffic light detection module810 and a distance determination module 815. In some embodiments,traffic light detection module 810 and distance determination module 815may be separately provided, rather than being parts of image analysismodule 805. In some embodiments, image analysis module 805 may includeother dedicated modules, for example, for extracting various featuresfrom captured images.

Memory 140 may also include a time determination module 820, adeceleration determination module 821, a suppression module 825, and asystem response module 830. The disclosed embodiments are not limited toany particular configuration of memory 140. Further, applicationprocessor 180 and/or image processor 190 may execute the instructionsstored in any of modules 805-830 included in memory 140. One of skill inthe art will understand that references in the following discussions toprocessing unit 110 may refer to application processor 180 and imageprocessor 190 individually or collectively. Accordingly, steps of any ofthe following processes may be performed by one or more processingdevices.

In one embodiment, image analysis module 805 may store instructions(such as computer vision software) which, when executed by processingunit 110, performs image analysis (e.g., monocular image analysis) of aset of images acquired by at least one of image capture devices 122,124, and 126. For example, image capture device 122 may acquire at leastone image of an area forward or ahead of a vehicle. An “area forward ofthe vehicle” includes any geographical region located in front of avehicle, relative to its moving direction. The region may include ajunction, an intersection, a crossroad, a traffic circle, a street, aroad, etc. Hereinafter, the term “intersection” may be interchangeablewith the term junction or crossroad where two or more roads meettogether. In some cases, the area forward or ahead of the vehicle mayinclude a plurality of traffic light fixtures each including at leastone traffic light. A “traffic light fixture” includes any form ofstructure housing one or more light-producing devices used to regulatetraffic and/or to provide road-related information. In some cases, twoor more traffic light fixtures may be joined in a single traffic lightassembly, but each traffic light fixture may be associated with adifferent lane. A typical traffic light fixture may include threecircular traffic lights: a green traffic light, a yellow traffic light,and a red traffic light. A “traffic light” includes a device having atleast one light source capable of displaying a distinctive color. Insome cases, the vehicle may encounter a non-typical traffic lightfixture. A non-typical traffic light fixture may include one or morenon-circular traffic lights having different colors. For example, aright-turn arrow traffic light, a left-turn arrow traffic light, apublic transportation traffic light, a pedestrian crossing trafficlight, a bicycle crossing traffic light, etc.

In some embodiments, processing unit 110 may combine information from aset of images with additional sensory information (e.g., informationfrom radar) to perform the image analysis. Image analysis module 805 mayinclude instructions for detecting a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, distances, andany other features associated with an environment of a vehicle. In someembodiments, image analysis module 805 may include individual modulesfor detecting different features from the captured images.

In one embodiment, traffic light detection module 810 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs image analysis of one or more imagesacquired by at least one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may analyze the one or more imagesto detect a shape of the traffic light, a color of the traffic light, atransition of colors, a location of the traffic light, etc. Processingunit 110 may determine the status of a traffic light (red, green,yellow, or transitioning from green to yellow, yellow to red, or red togreen). Processing unit 110 may also detect, from the one or moreimages, the relevancy of a traffic light to vehicle 200. For example,processing unit 110 may determine whether a traffic light regulates alane in which vehicle 200 is traveling. Based on the analysis,processing unit 110 may cause one or more system responses in vehicle200, such as a warning to a driver, or other navigational responses suchas a turn, a lane shift, an acceleration, a deceleration (e.g., by abrake), maintaining current speed, and the like, as discussed below inconnection with system response module 830.

Distance determination module 815 may store software configured toanalyze one or more images captured by at least one of image capturedevices 122, 124, and 126. In some embodiments, processing unit 110 mayanalyze the images to determine a distance from vehicle 200 to anintersection (also referred to as a junction). The term “distance to anintersection” or “distance to a junction” refers to the distance fromthe vehicle to a starting location of the junction. For example, in somesituations, the starting location of the junction is marked with a stopline, or a pedestrian crossing line. In such situations, the distance tothe junction refers to the distance from the vehicle to the stop line orpedestrian crossing line. In other situations, the starting location ofthe junction is not marked with a stop line or a pedestrian crossingline. In such situations, the distance to the junction refers to thedistance from the vehicle to the starting location where the stop lineor pedestrian crossing line is supposed to be.

The distance to the junction (e.g., the end of the current lane in whichthe vehicle is travelling that meets one or more lanes of one or morecrossing roads) may be determined based on the distance from the vehicleto the stop line (or pedestrian crossing line), if such a marking isvisible on the road, as captured in the images. In some embodiments, thedistance to the junction may be determined based on the distance fromthe vehicle to a reference point which is at a certain distance (e.g.,10 cm) from the stop line in the images. When the stop line orpedestrian crossing line is not visible on the road (or is not visiblein the captured images), the distance to the junction may be determinedbased on the distances of the vehicle to one or more traffic lights ortraffic light fixtures on which the traffic lights are attached, whichare included in the captured images. In yet some embodiments, whether astop line exists on the ground or not, the reference point forcalculating the distance from the vehicle to the junction may be acertain distance, fixed or not, from the point where good visibility ofcrossing traffic or any other visual or infrastructure related conditionor criterion is met, specifically when the reference point and thelocation of the host vehicle relative to the reference point can bedetermined from an image of an environment of the junction, as capturedby an imaging device onboard the vehicle approaching the junction. Basedon the distance to the junction, system 100 (e.g., via processing unit110) may determine whether one or more conditions for causing a systemresponse are satisfied. The system response may include a warning oralert notification to a driver, or other navigational responses such asa turn, a lane shift, an acceleration, a deceleration (e.g., by abraking), maintaining current speed, and the like, as discussed below inconnection with system response module 830.

When system 100 determines that the one or more conditions aresatisfied, system 100 may send a control signal to a suitable componentof vehicle 200, e.g., a display, a speaker, a vehicle control systemsuch as throttling system 220, braking system 230, and/or steeringsystem 240, to cause the system response. For example, a visual alertmessage may be displayed on the on-board display to the driver.Alternatively or additionally, an auditory message may be sounded toalert the driver. In some embodiments, system 100 may cause an automaticnavigational response (regardless of whether the vehicle is aconventional vehicle operated by a driver or an autonomous vehicle),such as braking to decelerate, accelerating, shifting a lane, ormaintaining the current speed. For example, when a red traffic light isdetected at a certain distance ahead of the vehicle, system 100 maycause the vehicle to gradually reduce its speed, e.g., at a certaindeceleration, such as 2.5 m/s², 3.0 m/s², 3.5 m/s², or any othersuitable deceleration that system 100 may determine based on the currentspeed of the vehicle and the distance from the vehicle to the junction.In some embodiments, system 100 may dynamically calculate thedeceleration needed for the vehicle to stop before the junction as thevehicle moves closer to the junction. For example, system 100 mayre-calculate the deceleration needed whenever the vehicle travels 10meters toward the junction, and cause the vehicle to dynamically updatethe deceleration with the re-calculated value.

In some embodiments, processing unit 110 may execute instructions toanalyze images showing the intersection including the traffic light todetermine a present distance from the vehicle to the intersection (e.g.,to a stop line, a traffic light, or a traffic light fixture) using anoptical flow method or any other methods known in the art that canestimate the distance based analysis of the captured images.Additionally or alternatively, the distance from the vehicle to theintersection (e.g., to the stop line, the traffic light, or the trafficlight fixture) may be determined using other sensors, such as radarand/or laser sensors, infrared sensors, GPS sensors, etc.

In one embodiment, time determination module 820 may store softwareexecutable by processing unit 110 to determine an estimated amount oftime until vehicle 200 will reach an intersection associated with thetraffic light fixture. For example, processing unit 110 may analyze aplurality of images using an optical flow method to determine theestimated amount of time (also referred to as time to contact or TTC).In some embodiments, processing unit 110 may determine the estimated TTCbased on an expansion of a focus of field of view, as derived fromanalyzing a plurality of images (e.g., sequentially acquired images). Insome embodiments, processing unit 110 may determine the TTC based on thecurrent distance from the vehicle to the intersection (e.g., to the stopline, to the traffic light, or to the traffic light fixture), thecurrent speed of the vehicle, and the current acceleration of thevehicle using any methods known in the art. Processing unit 110 maycompare the TTC with a predetermined time threshold. Based on thecomparison, e.g., when TTC is smaller than or equal to the predeterminedtime threshold, processing unit 110 may cause one or more systemresponses in the vehicle, such as a warning to a driver, or othernavigational responses such as a turn, a lane shift, an acceleration, adeceleration (e.g., by braking), maintaining current speed, and thelike, as discussed below in connection with system response module 830.

In one embodiment, deceleration determination module 821 may storesoftware executable by processing unit 110 to determine a minimumdeceleration required to be applied to the vehicle, such that thevehicle can stop before reaching the intersection (e.g., at the end ofthe current distance from the vehicle to the stop line, as estimated bydistance determination module 815). Processing unit 110 may compare theminimum deceleration with a predetermined deceleration threshold. Basedon the comparison (e.g., when the minimum deceleration is greater thanor equal to the predetermined deceleration threshold), processing unit110 may cause one or more system responses in vehicle 200, such as awarning to a driver, or other navigational responses such as a turn, alane shift, an acceleration, a deceleration (e.g., by a brake),maintaining current speed, and the like, as discussed below inconnection with system response module 830.

In some embodiments, processing unit 110 may determine whether to causea system response based on both the TTC and the minimum deceleration.For example, processing unit 110 may cause a system response when boththe TTC is equal to or less than a predetermined time threshold and theminimum deceleration is equal to or greater than a predetermineddeceleration threshold.

In one embodiment, suppression module 825 may store software executableby processing unit 110 to determine whether a system response should besuppressed. Suppression module 825 may store various pre-set suppressionschemes. Processing unit 110 may determine whether information detectedfrom captured images and other sensors satisfies pre-set requirementsfor suppressing a system response, such that processing unit 110 doesnot cause a system response (e.g., an alert or warning notification tothe driver, acceleration, deceleration, etc.) in the vehicle. Forexample, the pre-set suppression schemes may include at least one ofsuppressing a system response based on vision information (e.g.,information included in the images captured by the cameras) andsuppressing a system response based on driver behavior that indicatesthat the driver is taking control of vehicle 200.

In one embodiment, system response module 830 may store softwareexecutable by processing unit 110 to cause a system response. Forexample, based on at least one of traffic light status (e.g., asdetermined by traffic light detection module 810), a distance fromvehicle 200 to a traffic light fixture (e.g., as determined by distancedetermination module 815), TTC estimation (e.g., as determined by timedetermination module 820), minimum deceleration required for vehicle 200to stop before entering the intersection (e.g., as determined bydeceleration determination module 821), and determination related tosuppression schemes (e.g., as determined by suppression module 825),processing unit 110 may determine whether to cause a system response invehicle 200. If it is determined that a system response should be causedin vehicle 200, system response module 830 may send a signal to acomponent of vehicle 200 to cause the response. For example, when it isdetermined that a warning or alert notification should be provided tothe driver of vehicle 200 about a red traffic light ahead of vehicle200, processing unit 110 may cause an alert to be presented to thedriver. The alert may be presented to the driver as a visual alert(e.g., displaying a warning sign on an on-board display or flashing alight or an icon on an instrument panel), an audio alert (e.g.,providing a beeping sound through a speaker), a tactile alert (e.g.,causing vibration of the driver's seat or vibration of the steeringwheel), or any combination of these alerts.

In some embodiments, processing unit 110 may cause one or morenavigational responses in vehicle 200, such as a turn, a shift of lane,an acceleration, a deceleration, a braking, maintaining current speed,etc. Processing unit 110 may provide control signals to throttlingsystem 220, braking system 230, and/or steering system 240 to cause thesystem response. For example, processing unit 110 may transmitelectronic signals that cause system 100 to physically apply the brakesby a predetermined amount or ease partially off the accelerator ofvehicle 200. Further, processing unit 110 may transmit electronicsignals that cause system 100 to steer vehicle 200 in a particulardirection. Such responses may be based on the determination of therelevant traffic light. Further, processing unit 110 may determine thatit is safe to pass the intersection if the current speed is maintained,and may cause throttling system 220 to maintain the current speed.

In some embodiments, system 100 may distinguish between relevant andirrelevant (or less relevant) traffic lights. During a typical drivingsession, vehicle 200 may cross one or more junctions or intersectionshaving multiple traffic lights. For example, one or more of the trafficlights at an intersection may regulate traffic traveling toward theintersection in a particular direction. Accordingly, one or more trafficlights may regulate whether vehicle 200 may continue to travel throughthe intersection or whether vehicle 200 must stop at the intersection.However, in addition to the traffic lights that regulate the lane inwhich vehicle 200 is traveling, other traffic lights that regulatetraffic in other lanes may be visible to vehicle 200.

Navigating vehicle 200 according to any of the traffic lights thatregulate lanes other than the lane in which vehicle 200 is traveling mayresult in navigational responses undesirable or inapplicable to theintended route of vehicle 200. Accordingly, to enable control of vehicle200 in a manner appropriate to the intended navigational path of vehicle200, system 100 may identify which of a plurality of traffic lights isregulating traffic in the lane in which vehicle 200 is traveling whiledisregarding other traffic lights that regulate other lanes of traffic.In some embodiments, system 100 may allocate less processing resourcesor assign a lower processing priority to the other traffic lightsdetected in the images that regulate other lanes of traffic. In someembodiments, system 100 may assign a lower weight for a driving scenariorelated to the other traffic lights detected in the images that regulateother lanes of traffic. Further, after system 100 identifies a relevanttraffic light, system 100 may identify a status of the traffic light(e.g., red, yellow, green) and implement an appropriate navigationalresponse. For example, system 100 may discontinue cruise control andapply the brakes when a red light is recognized that regulates the lanein which vehicle 200 is traveling or when a yellow light is recognizedthat regulates the lane in which vehicle 200 is traveling and vehicle200 is beyond a predetermined distance of a junction. In someembodiments, system 100 may compute several scenarios related todifferent traffic lights detected in the images, and may assign eachscenario with a different likelihood of being relevant to vehicle 200.Optionally, system 100 may also compute one or more possiblenavigational responses associated the different traffic lights indifferent scenarios. As vehicle 200 travels toward the traffic lights,the scenarios may be dynamically updated by system 100. As the scenariosprogress, some navigational responses may gradually receive a higherscore, while others may become less likely (e.g., receive a lowerscore), or are even eliminated altogether.

Distinguishing between relevant and irrelevant (or less relevant)traffic lights on a road may be complex. FIG. 9 illustrates examples oftraffic light detection. In FIG. 9, vehicle 200 a is traveling on amultilane road. Each lane of the road is associated with a differenttraffic light fixture. Vehicle 200 a is approaching an intersection andis traveling in a lane designated for proceeding through theintersection and to the opposite side across the intersection. Alsoshown in FIG. 9, vehicle 200 b is traveling in a lane that allowstraffic to continue to travel straight and through the intersection orthat allows traffic to make a right turn. The traffic light fixtureassociated with the lane in which vehicle 200 b is traveling includes aright-turn traffic light. As another example, in FIG. 21, vehicle 200 areaches a junction of non-perpendicular roads. Multiple traffic lightfixtures (e.g., traffic light fixtures 912 and 924), including some thatdo not regulate the lane in which vehicle 200 a is traveling, may bevisible to vehicle 200 a due to the orientation of the junction.

Returning to FIG. 9, an intersection 900 is shown that has the followingdriving options: road A has lanes leading to roads B, D, and F; road Chas lanes leading to roads D and F; road E has lanes leading to roads F,H, and B; and road G has lanes leading to roads H, B, and D. Road A isassociated with four traffic light fixtures 912, 914, 916, and 918. Inthe situation shown, each traffic light fixture regulates a differentlane. Road C is associated with two traffic light fixtures 922 and 924.Traffic light fixture 924 includes a traffic light 924 a for continuingstraight and a traffic light 924 b for right turns (e.g., displaying agreen arrow when a right turn is authorized). Road E is associated withthree traffic light fixtures 932, 934, and 936. And road G is associatedwith two traffic light fixtures 942 and 944. Traffic light fixture 942includes a traffic light 942 a for continuing straight and a trafficlight 942 b for left turns. In the situation illustrated in FIG. 9,traffic light 924 b and traffic light fixtures 912, 918, and 932 displaygreen lights while all the other traffic light fixtures display redlights. Of course, many other road variations and relative traffic lightconfigurations may exist in addition to those shown in FIG. 9.

In the situation illustrated in FIG. 9, vehicle 200 a is located on roadA in a lane that continues straight through intersection 900 to road D.However, the field-of-view of an image capture device included invehicle 200 a may include both traffic light fixtures 912 and trafficlight fixture 914 (or even additional fixtures). Vehicle 200 a arrivedat intersection 900 when both traffic light fixtures 912 and 914displayed red lights, and only recently the light in traffic lightfixture 912 has turned green. In this situation, it is important thatthe traffic light detection system 100 of vehicle 200 a recognizes thatthe green light of fixture 912 is not applicable to vehicle 200 a.Rather, system 100 should base any determined system response (includingwarning to driver and other navigational response) on the status of thetraffic light associated with the more relevant fixture 914.

In another aspect of the situation illustrated in FIG. 9, vehicle 200 bis located on road C in a lane that continues straight throughintersection 900 to road F, and that lane also allows traffic to make aright turn to road D. Vehicle 200 b faces traffic light fixture 924 thatincludes a traffic light 924 a for continuing straight and a trafficlight 924 b for right turns. Vehicle 200 b arrived at intersection 900when traffic light fixture 924 displayed red lights for continuingstraight (traffic light 924 a) and for right turns (traffic light 924b), and only recently traffic light 924 b has turned green. This meansthat the current status of traffic light fixture 924 prohibits trafficfrom driving straight and turning right. An undesirable situation mightoccur if vehicle 200 b acts on the status of light 924 b withoutrecognizing and accounting for the status of light 924 a. For example,if vehicle 200 b drives to road F (i.e., drives straight) based on theinformation of traffic light 924 b (showing a green light), vehiclesdriving from road E to road B may create a hazard for vehicle 200 b.

The situations depicted in FIG. 9 provide just a few examples of roadsituations in which it may be desirable to have a system that candistinguish between relevant and irrelevant traffic light fixtures, todetermine the status of the traffic lights included in the relevanttraffic light fixture(s), and to cause vehicle 200 to take anappropriate system response based on the status and relevancydetermination. Further, these examples demonstrate that the system mayneed to evaluate multiple traffic light fixtures that may face a vehiclein order to identify the traffic light fixture that is most applicableto the lane in which the vehicle is traveling or to the intended traveldirection of the vehicle (especially where, for example, multipletraffic lights or traffic light fixtures may be associated with a singlelane in which the vehicle is traveling).

As discussed above, system 100 may distinguish between relevant andirrelevant traffic light fixtures, determine the status of the trafficlights included in the relevant traffic light fixture(s), and causevehicle 200 to take an appropriate system response based on the statusand relevancy determination in various driving scenarios. For example,as vehicle 200 (e.g., 200 a or 200 b) approaches an intersection, system100 may determine which traffic light is relevant, determine a status ofthat traffic light, and find any other relevant information in imagescaptured by one or more of image capture devices 122-126. If the trafficlight is red, system 100 may cause vehicle 200 to apply its brakes. Ifthe traffic light is green, system 100 may cause vehicle 200 tocontinue. If the traffic light is yellow, system 100 may determine adistance to the intersection and/or an estimate time to the intersectionbased on analysis of images, the speed of vehicle 200, and/or positionaldata (e.g., GPS data).

If vehicle 200 is within a predetermined time (e.g., five seconds, tenseconds, etc.) and/or distance (e.g., one meter, five meters, tenmeters, etc.) threshold, system 100 may cause vehicle 200 to continue topass the intersection (e.g., by maintaining the current speed anddirection or by accelerating). If vehicle 200 is not within thepredetermined time threshold and/or distance threshold, system 100 maycause vehicle 200 to stop before entering the intersection. As anotherexample, when vehicle 200 is stopped at a traffic light, after thetraffic light changes its status from red to green, system 100 may causea navigational response that includes applying the accelerator,releasing the brakes, and steering through an intersection, for example.

As described above, traffic light detection module 810 may detecttraffic light from one or more images captured by a camera, and maydetermine the relevancy of the detected traffic light to the lane inwhich vehicle 200 is traveling. For example processing unit 110 maydetermine the relevancy of each of the plurality of traffic lightfixtures (or traffic lights) captured within one or more images tovehicle 200.

For purposes of this disclosure, determining the relevancy of trafficlight fixtures may include executing one or more assessments. In oneembodiment, processing unit 110 may assess an orientation of each of aplurality of traffic light fixtures with respect to vehicle 200. Forexample, in the situation illustrated in FIG. 9 the field-of-view(indicated by the dotted lines) of a camera installed on vehicle 200 aincludes both traffic light fixture 912 and traffic light fixture 914.However, traffic light fixture 912 may appear farther away from a centeraxis of the camera installed on vehicle 200 a than traffic light fixture914 (e.g., an angle between traffic light fixture 912 and center axis ofthe camera may be greater than an angle between traffic light fixture914 and the center axis). The orientation (such as the angle) related totraffic light fixture 914 suggests that traffic light fixture 914 ismore relevant to vehicle 200 a than fixture 912.

Other assessments may be applicable to the decision making process. Forexample, processing unit 110 may assess the distance of each of theplurality of traffic light fixtures with respect to the vehicle. Forexample, processing unit 110 may execute instructions installed withindistance determination module 815 to assess the distance between thevehicle and each traffic light fixture. Processing unit 110 maydetermine that a closest traffic light fixture (e.g., having thesmallest distance to the vehicle) is the most relevant to the vehicle.Additionally or alternatively, processing unit 110 may assess whichtraffic light is facing a front portion of vehicle 200.

Additionally or alternatively, processing unit 110 may use theidentified lane markers to divide the area forward of vehicle 200 to aplurality of zones, associate each identified traffic light fixture witha zone, and assess which zone is the most relevant for vehicle 200.Additionally or alternatively, processing unit 110 may compare a vehiclelocation that was acquired via GPS or any other mapping or localizationinformation and associated technology such as Road Experience Managementtechnology, to map data to determine the relevancy of the traffic light.For example, vehicle 200 may access map data that includes informationabout the possible driving options at a number of locations. By usingthe GPS acquired vehicle location, processing unit 110 may determinewhich driving options are available to vehicle 200 approaching ajunction, and use this information to determine the relevancy of thetraffic lights at the junction to vehicle 200.

In one embodiment, traffic light detection module 810 may store softwareinstructions which, when executed by processing unit 110, determine astatus of a traffic light included in at least one traffic lightdetermined to be relevant to the vehicle. As described above, the statusof the traffic light may be also associated with the information thetraffic light provides. In a typical traffic light, the color beingdisplayed and the relative location of the illuminated traffic light mayprovide basic information relevant to vehicle 200.

Traffic light detection module 810 may derive additional informationfrom the environment of the relevant traffic light fixture and fromnon-typical traffic light included in the relevant traffic lightfixture. For example, the relevant traffic light fixture may have in itsproximity a sign that includes relevant text, e.g., a sign statingspecific days and hours. Accordingly, in some embodiments, traffic lightdetection module 810 may derive information from the text included insigns associated with the relevant traffic fixture. For example, trafficlight detection module 810 may implement optical character recognitiontechniques to recognize text in the signs. Traffic light detectionmodule 810 may then compare the recognized text to a database todetermine the information provided by the sign.

The location of the traffic light fixtures in a junction can be before,after, or in the middle of the junction. Identifying the position ofeach traffic light fixture in the junction may be used, for example, todetermine the relevancy of the traffic light fixtures. In someembodiments, processing unit 110 may estimate the distance of one ormore traffic light fixtures with respect to vehicle 200 to create a 3Dmodel of a junction. In one embodiment, the 3D model of the junction maybe stored for future usage. The 3D model of the junction may include oneor more of the following: a 3D position for one or more traffic lightfixtures, the relative distance between each traffic light fixture andother traffic light fixtures in the junction, the direction(s) eachtraffic light fixture refers to, and the relative distance between eachtraffic light fixture and the stop line of the junction.

In addition, the 3D model may be periodically updated using detailsrecognizable when vehicle 200 approaches the junction. Examples of therecognizable details may include arrows in traffic lights, the lanemarking near or in the junction, etc. In one example, when time vehicle200 passes the junction, recognized details are compared to theinformation stored in the 3D model, and if appropriate, the 3D model isupdated.

Consistent with embodiments of the present disclosure, the 3D model maybe used to determine the relevancy of one or more traffic lightfixtures. In order to activate braking system 230 with sufficient timeto stop vehicle 200 before the junction, system 100 may determine whichtraffic light is relevant to vehicle 200 at a distance of about sixty toeighty meters from the junction. As vehicle 200 approaches a junction,information derived from the captured images may be compared to the 3Dmodel to find a match. For example, processing unit 110 may compare therelative distance between recognized traffic light fixtures in thejunction to the 3D model to determine the relevancy of one or moretraffic light fixtures.

Using the 3D model, processing unit 110 may identify a relevant trafficlight when vehicle 200 is more than a predetermined distance (e.g., 50meters, 75 meters, 100 meters, or 125 meters) from the junction. Asanother example, processing unit 110 may compare the relative distancebetween recognized traffic light fixtures in the junction to the 3Dmodel to determine the distance to the junction's stop line, even whenthe stop line is not visible from the current location of vehicle 200.Using the 3D model, processing unit 110 may determine the distance tothe junction's stop line when vehicle 200 is more than the predetermineddistance (e.g., 50 meters, 75 meters, 100 meters, or 125 meters) fromthe junction.

As discussed above with reference to FIG. 8, processing unit 110 mayidentify traffic lights in at least one acquired image of an areaforward of vehicle 200. For example, processing unit 110 may filterobjects identified in the at least one acquired image to construct a setof candidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like.

In addition, processing unit 110 may analyze the geometry of the areaforward of vehicle 200. The analysis may be based on any combination of:(i) the number of lanes detected on either side of vehicle 200, (ii)markings (such as arrow marks) detected on the road, and (iii)descriptions of the area extracted from map data. For example, if lanemarkings defining a lane of travel are recognized on a road, and atraffic light fixture is within the boundaries of the lane markings,system 100 may conclude that the traffic light fixture is associatedwith the lane associated with the lane markings.

In some embodiments, processing unit 110 may determine at least oneindicator of vehicle position from the images. An “indicator of vehicleposition” includes any form of information related to the physicallocation of a vehicle. The indicator of vehicle position may be derivedfrom analyzing the image data (e.g., the at least one acquired image).Additionally, the indicator of vehicle position may be derived fromgeographic position data (e.g., GPS signals, local positioning signals,and/or map data) or from data indicative of the position of vehicle 200relative to other vehicles on the road.

In some embodiments, the indicator of vehicle position may include thedistance to at least one traffic light fixture derived from the at leastone image. In other embodiments, the at least one indicator of vehicleposition may include a lane marker recognized based on analysis of theat least one image. For example, processing unit 110 may conduct ananalysis using information derived from the at least one image toidentify one or more lane markers. Using the identified lane markers,processing unit 110 may determine a correspondence between the detectedtraffic lights and the lane vehicle 200 is currently driving.

In some embodiments, processing unit 110 may use the at least oneindicator of the vehicle position to determine the relevancy of each ofthe plurality of traffic light fixtures to vehicle 200. In someembodiments, processing unit 110 may rank the relevancy of the trafficlight fixtures identified in the at least one acquired image. Thetraffic light fixture having the highest value of relevancy ranking maybe determined to be the relevant traffic light fixture. For example, inthe situation illustrated in FIG. 9 the field-of-view of image capturedevice 122 includes both traffic light fixture 912 and traffic lightfixture 914, but by using one or more assessments, processing unit 110may determine that traffic light fixture 914 has a higher relevancyranking value.

One way for processing unit 110 to determine that traffic light fixture914 has a higher relevancy ranking than traffic light fixture 912 is byassessing the distance of each of the plurality of traffic lightfixtures with respect to vehicle 200 a. For example, in the situationdepicted in FIG. 9, traffic light fixture 914 is closer than trafficlight fixture 912. Thus, traffic light fixture 914 is more likely to berelevant than traffic light fixture 912.

In some embodiments, the relevancy determination of each of theplurality of traffic light fixtures to vehicle 200 may include apreliminary examination to eliminate improbable traffic light fixtures.For example, when the at least one acquired image includes three closetraffic light fixtures and two distant traffic light fixtures, the twodistant traffic light fixtures may be classified as improbable to berelevant to vehicle 200. By eliminating improbable traffic lightfixtures and ranking the relevancy of a subject of the traffic lightfixtures identified in the at least one acquired image, processing unit110 may save processing power.

In some embodiments, the relevancy ranking may change when vehicle 200approaches the junction. For instance, the orientation of the trafficlight fixture may change from different points on the road, thus, thedistance to the junction may impact the probability that a given trafficlight fixture is relevant to vehicle 200. Accordingly, the relevancyranking may be associated with a confidence level, which may take intoaccount factors, such as distance to a junction, when assessing trafficlight fixtures. Further, processing unit 110 may periodically orconstantly update the relevancy ranking of the traffic light fixtureswhen the confidence level is below a certain predetermined threshold.

Traffic light detection module 810 may further use navigationinformation previously stored within system 100, such as within memory140/150, in order to determine a traffic light relevant to vehicle 200.Based on a determined 3D position of vehicle 200 and/or image capturedevices 122-126 as discussed above, traffic light detection module 810may perform a registration between navigational map data and vehicle200. Based on the determination of relative distance measurements asdescribed above, traffic light detection module 810 may use thenavigational map data to determine the point in 3D space at whichvehicle 200 (via braking system 230) should brake in order to stop ateach detected traffic light fixture at the junction. According to the 3Dregistration result, traffic light detection module 810 may determine alane assignment for each traffic light detected at the intersectionusing the navigational map data. Traffic light detection module 810 maythen determine the lane assignments within system 100 and imageprocessor 190, then perform the registration.

Processing unit 110 (e.g., via traffic light detection module 810) maydetermine, based on the at least one acquired image, a status of atraffic light included in at least one traffic light fixture determinedto be relevant to vehicle 200. In some cases, the relevant traffic lightfixture may include a plurality of illuminated traffic lights, and thestatus of each traffic light may depend on the type of the trafficlight. In one embodiment, the status of a traffic light means simply thecolor it indicates, e.g., green, yellow, and red. System 100 mayidentify the status of a traffic light using a variety of techniques.For example, system 100 may identify an area of one or more images thatinclude a traffic light and perform an analysis of the pixels in thearea to determine the colors of the pixels. After analyzing at least athreshold number of pixels (e.g., two pixels, ten pixels, twenty pixels,etc.) in the area, system 100 may, for example, determine the color ofthe traffic light by finding an average value of the pixels in the area.

Additionally or alternatively, the status of the traffic light mayinclude the direction the traffic light refers to. In a typical trafficlight, a green color indicates that vehicle 200 is allowed to proceed.However, sometimes this information by itself is insufficient to decidewhether it is safe to drive in a certain direction, such as when a greenlight only authorizes a turn. One way to determine which direction thetraffic light refers to includes accessing a database that correlateseach traffic light with one or more directions.

Another way includes identifying, from the image data, the type of thetraffic light and determining from the contextual situation thedirection the traffic light refers to. For example, in the secondsituation depicted in FIG. 9 relative to vehicle 200 b, traffic lightfixture 924 may be determined as the relevant traffic fixture, buttraffic light fixture 924 includes two illuminated traffic lights 924 aand 924 b. Accordingly, processing unit 110 may determine that thestatus of traffic light 924 a is a red light for continuing straight,and the status of traffic light 924 b is a green light for turningright. In some embodiments, the determination of the status of thetraffic light includes one or more of determining a location of thetraffic light within a relevant traffic light, determining whether thetraffic light is illuminated, determining a color of the traffic light,and determining whether the traffic light includes an arrow. In someembodiments, the traffic light may not include an arrow in itself, butthere may be an auxiliary light or sign that is disposed adjacent (e.g.,above or next to) the traffic light to indicate (e.g., with arrows) thedirection to which the traffic light applies. Processing unit 110 maydetect the traffic light and the auxiliary light or sign to determinewhether the traffic light is green and the direction of the trafficlight. In some embodiments, processing unit 110 may detect markings(e.g., arrows or words) on the surface of the road that indicate thedirection of a respective lane and to which the respective traffic lightapplies. Processing unit 110 may determine the status and direction ofthe traffic light based on analysis of the image of the traffic lightand the image of the markings on the road surface.

Processing unit 110 (e.g., via traffic light detection module 810) maycause a system response based on the determined status. In someembodiments, processing unit 110 may cause one or more system responses(e.g., two or more responses), including responses of different types.One type of system response may include navigational responses, thenavigational response may include, for example, starting to drive, achange in acceleration, a change in velocity, applying vehicle brakes,discontinuing cruise control, and the like. For example, these systemresponses may include providing control signals to one or more ofthrottling system 220, braking system 230, and steering system 240 tonavigate vehicle 200.

Another type of system response may include initiating a timer and/orincrementing a counter in order to provide statistical information aboutone or more driving sessions to the driver of vehicle 200. For example,the statistical information may indicate how many times vehicle 200 hasencountered red lights in a driving session, and/or the duration of adriving session that vehicle 200 spent waiting at red lights.

Another type of system response may include providing variousnotifications (e.g., warnings and/or alerts) to the driver of vehicle200. The warnings and/or alerts may include, for example, announcing thecolor of a relevant traffic light and/or a distance to a junction. Thenotifications may be provided via speakers 360 or via an associateddisplay (e.g., touch screen 320).

System 100 may detect that a vehicle, such as vehicle 200, is travelingin a turn lane. A “lane” may refer to a designated or intended travelpath of a vehicle and may have marked (e.g., lines on a road) orunmarked boundaries (e.g., an edge of a road, a road barrier, guardrail, parked vehicles, etc.), or constraints. System 100 may operate tomake these detections and determinations based on visual informationacquired via one or more image capture devices, for example. In someembodiments, these detections and determinations may also be made atleast in part on map data and/or sensed vehicle position. In addition todetermining the status of vehicle 200 as being in a turn lane, system100 may recognize a traffic light associated with the lane, and may beconfigured to determine the status of the traffic light based onanalysis of road context information and determined characteristics ofthe traffic light.

FIG. 10 illustrates a further example of traffic light detection. FIG.10 shows a vehicle 200 traveling on a roadway 1000 in which thedisclosed systems and methods for detecting an object in the roadway maybe used. Vehicle 200 is depicted as being equipped with image capturedevices 122 and 124; more or fewer cameras may be employed on anyparticular vehicle 200. As shown, roadway 1000 may be subdivided intolanes, such as lanes 1001 and 1011. Lanes 1001 and 1011 are shown asexamples; a given roadway 1000 may have additional lanes based on thesize and nature of the roadway. In the example of FIG. 10, vehicle 200is traveling in lane 1011 and vehicle 200 e is traveling in land 1001,and both vehicles 200 and 200 e are approaching intersection 1002.Traffic in lane 1011 and lane 1001 at intersection 1002 is regulated bytraffic light fixture 1006. System 100 may, as discussed in detailbelow, determine the status of traffic light fixture 1006 and cause asystem response affecting operations of vehicle 200. Further illustratedon roadway 1000 are warning lines 1010 (or pedestrian crossing lines1010) leading to a stop line 1012. In some embodiments, stop line 1012may be in front of warning lines 1010 (i.e., on the other side ofwarning lines 1010 closer to the vehicles shown in FIG. 11)

Processing unit 110 may be configured to determine one or more laneconstraints associated with each of lanes 1001 and 1011 and intersection1002 based on a plurality of images acquired by one or more of imagecapture device 122-126 that processing unit 110 may receive via datainterface 128. According to some embodiments, the lane constraints maybe identified by visible lane boundaries, such as dashed or solid linesmarked on a road surface. Additionally or alternatively, the laneconstraints may include an edge of a road surface or a barrier.Additionally or alternatively, the lane constraints may include markers(e.g., Botts' dots). According to some embodiments, processing unit 110may determine constraints associated with lanes 1001/1011 and/orintersection 1002 by identifying a midpoint of a road surface width,such as the entirety of roadway 1000 or intersection 1002 or a midpointof one of lanes 1001/1011. Processing unit 110 may identify laneconstraints in alternative manners, such as by estimation orextrapolation based on known roadway parameters when, for example, linesdesignating road lanes such as lanes 1001/1011 are not painted orotherwise labeled. Processing unit 110 may also determine the physicalrelative distance between the various constraints and detected objects,such as traffic light lights.

Distance estimation for a junction with traffic lights may bechallenging when developing autonomous vehicle or red traffic lightwarning systems, because the location of the traffic light may be afteror in the middle of the junction. For example, determining the 3Dposition for each traffic light at an intersection may imply a brakingpoint for the vehicle and the stop line may provide an accurateposition.

In the present system, detection of the constraints and pathways ofroadway 1000, cross street 1004, or intersection 1002, as well asconstituent lanes 1001/1011 may include processing unit 110 determiningtheir 3D models via a camera coordinate system. For example, the 3Dmodels of lanes 1001/1011 may be described by a third-degree polynomial.In addition to 3D modeling of travel lanes, processing unit 110 mayperform multi-frame estimation of host motion parameters, such as thespeed, yaw and pitch rates, and acceleration of vehicle 200. Processingunit 110 may further determine a road elevation model to transform theinformation acquired from the plurality of images into 3D space.

One feature of the present system is that a global model may be createdfor static objects at a traffic intersection simultaneously rather thancreating one standalone model for every detected object. System 100 maythus be configured to determine absolute distances for objects or laneconstraints at a distance, for example, within about one hundred meters.In some cases, system 100 may be configured to determine absolutedistances for objects or lane constraints at other distances (e.g.,within 125 meters, within 150 meters, etc.). Further, system 100 may beconfigured to determine which traffic light fixture (e.g., traffic lightfixture 1006) is relevant to vehicle 200 within a distance of aboutsixty to eighty meters from stop line 1012.

Accurate distance estimation to the intersection (e.g., to the trafficlights at the intersection and/or stop line) may be achieved throughprocesses referred to as expansion and scaling. The expansion processmay use relative distances to determine a distance from vehicle 200 toan object, such as stop line 1012. For example, if two traffic lightfixtures are detected in the image data, and the distance between themincreases by 5% when the vehicle moves a distance of ten meters, thenthe system calculates that the distance is 200 meters.

In some embodiments, traffic light detection module 810 may storeinstructions which, when executed by processing unit 110, may detect thepresence and status of a traffic light fixture, such as traffic lightfixture 1006. Traffic light detection module 810, along with imageprocessor 190, may perform image processing on one or more imagesacquired by one or more of image capture devices 122-126. Traffic lightdetection module 810 may further determine the status of traffic lightfixture 1006, including a determination of whether one of the lightswithin traffic light fixture 1006 includes an arrow.

Traffic light detection module 810 may determine other informationrelevant to the status of traffic light fixture 1006, including but notlimited to whether any of the traffic lights associated within trafficlight fixture 1006 are illuminated (in either a solid or blinkingmanner), determining positions of the traffic lights within the trafficlight fixture (i.e. a horizontal orientation of traffic lights versus avertical orientation), or determining a color associated with thetraffic light. In some embodiments, traffic light detection module 810may store information determined for a particular heading, a particularintersection 1002, and/or a particular traffic light fixture 1006 withina storage device, such as in memory 140/150. In these embodiments,previously determined and saved information may be used in the futurewhen vehicle 200 returns to the same intersection.

In some embodiments, a blinking traffic light may be determined throughimage analysis of multiple images acquired at a predetermined or knowncapture rate (e.g., analyzing images acquired 1 second, 1.5 seconds, 2seconds, etc., apart). For example, system 100 may analyze image data toidentify a pattern in illumination among a plurality of images. System100 may further determine a region of a captured image determined to bewithin the boundaries of a particular traffic light of interest in atraffic light fixture. System 100 may then determine the color of atraffic light through pixel analysis in the region determined to bewithin the boundaries of the traffic light of interest.

Processing unit 110 may determine a position of vehicle 200. In someembodiments, determining the position of vehicle 200 may includedetermining at least one indicator of vehicle position, either via avisual determination or through analysis of the at least one imagereceived from image capture devices 122-126 via data interface 128. Inthese embodiments, the at least one indicator of vehicle position mayinclude a distance from the vehicle to one or more lane constraints orlane markers associated with the current lane in which the vehicle istraveling, or to markers associated with an intersection, such aswarning lines 1010 and stop line 1012.

The distance from the vehicle to a location or object may be determinedbased on, for example, one or more of an analysis of image data, GPSinformation, or data from a position sensor. Further, the at least oneindicator of vehicle position may include an arrow associated with thetraffic light fixture having at least one associated traffic light.Additionally or alternatively, the at least one indicator of vehicleposition may include a vehicle location acquired by GPS or a likecoordinate system. In some embodiments, memory 140 and/or 150 may storelane constraint information determined for a particular roadway 1000 andits lanes 1001/1011. In these embodiments, previously determined andsaved information may be used in the future when vehicle 200 returns tothe same intersection. For example, GPS information may be used todetermine that vehicle 200 has returned to the same intersection.

Consistent with disclosed embodiments, processing unit 110 may use theinformation from the at least one indicator of vehicle position todetermine if a system response changing the operation of vehicle 200 isrequired or recommended. Additionally or alternatively, processing unit110 may receive information from other sensors (including positionsensor 130) or other systems indicative of the presence of otherfeatures in the image data, such as additional vehicles, curvature ofthe road, etc.

Processing unit 110 may take one or more actions relative to theoperation of vehicle 200 based on information received from one or moresources, such as position sensor 130, image processor 190, or trafficlight detection module 810. In some embodiments, traffic light detectionmodule 810 may provide information regarding the status of a trafficlight at an intersection, such as traffic light fixture 1006 atintersection 1002 discussed above. In some embodiments, image analysismodule 805 may provide a determination of whether the vehicle is in aturn lane, and whether the traffic light fixture 2406 includes an arrow.Image analysis module 805 may detect turn lane or arrow based onanalysis of images captured by one or more cameras.

Processing unit 110 may cause a system response affecting theoperational status of vehicle 200, such as causing system 100 to providecontrol signals to one or more of throttling system 220, braking system230, and steering system 240 to navigate vehicle 200 (e.g., byaccelerating, turning, etc.). For example, in some embodiments, system100 may determine that the turn lane traffic light authorizes vehicle200 to make a turn. In these embodiments, system response module 830 maysend an instruction to steering system 240, and steering system 240 mayexecute the instruction to turn vehicle 200 through intersection 1002into a new lane of travel associated with cross street 1004. In otherembodiments, the system response initiated and executed by systemresponse module 830 may include any or all of providing a visual oraudible notice to the operator of the vehicle, applying vehicle brakes,discontinuing a cruise control function, or initiating one or moreautomated turning maneuvers.

Additionally or alternatively, system response module 830 may sendinstructions to other systems associated with vehicle 200, such asbraking system 230, turn signals, throttling system 220, etc. In someembodiments, system response module 830 may instead provide a humanoperator (i.e., driver) of the vehicle with audio, visual, or tactilefeedback representative of the information gathered from the relevantsystems and/or sensors. The human operator may then act on this feedbackto turn the vehicle.

FIG. 11 provides an annotated view of the situation depicted in FIG. 10.Vehicle 200 is once again traveling in lane 1011 of roadway 1000, and isapproaching intersection 1002. Vehicle 200 is again equipped with imagecapture devices 122 and 124, although more or fewer devices may beassociated with any particular vehicle 200. For simplicity ofillustration, roadway 1000 in FIG. 11 is depicted as a one-way streetoriented from the top to the bottom of the page with two travel lanes1001 and 1011.

To the left side of roadway 1000 at intersection 1002 is traffic lightfixture 1006. Painted on the surface of lane 1011 in front of vehicle200 is a lane arrow 1008, indicating that lane 1011 is a left turn lane.Also painted or otherwise affixed on the surface of roadway 1000 arewarning lines 1010 leading to stop line 1012.

Consistent with disclosed embodiments, system 100 may be configured todetermine whether vehicle 200's travel lane approaching an intersection(here, lane 1011) is a turn lane; determine whether a traffic lightfixture (here, traffic light fixture 1006) regulates the intersection;determine the status of a traffic light in the traffic light fixture;and determine whether that traffic light includes an arrow.

System 100 associated with vehicle 200 may determine the position ofvehicle 200 within roadway 1000 via one or more of processing unit 110,position sensor 130, traffic light detection module 810. Additionally oralternatively, system 100 may gather information from at least oneindicator of vehicle position. As discussed above, the at least oneindicator of vehicle position may include a distance from vehicle 200 toone or more lane constraints or lane markers associated with the currentlane in which the vehicle is traveling (such as lane 1011), or tomarkers associated with an intersection 1002, such as warning lines 1010and stop line 1012. Further, the at least one indicator of vehicleposition may include an arrow associated with traffic light fixture1006. Additionally or alternatively, the at least one indicator ofvehicle position may include a vehicle location acquired by GPS or alike coordinate system.

In the illustration of FIG. 11, multiple indicators of the position ofvehicle 200 are present. One or more of image capture devices 122-126associated with vehicle 200 may be configured to capture a plurality ofimages of the area in front of vehicle 200 that may assist indetermining a position of vehicle 200. For example, the images mayinclude lane arrow 1008, which indicates that lane 1011 is a left turnlane for intersection 1002. The images may also include traffic lightfixture 1006, and may indicate that one or more of the individual lightswithin traffic light fixture 1006 includes an arrow suggestive of a turnlane situation. One or more of image capture devices 122-126 may furthercapture images of warning lines 1010 or stop lines 1012 associated witheither roadway 1000 or cross street 1004.

Further, system 100 via processing unit 110 may determine a position ofvehicle 200 within the turn lane by determining a distance s1 from asurface of vehicle 200 to stop line 1012, or s2 from a surface ofvehicle 200 e to stop line 1012. Distance determination module 815 maystore instructions that may be executed by processing unit 110 todetermine the distances s1 and/or s2. Still further, system 100 mayimplement an optical character recognition (OCR) process to obtain textincluded in one or more captured images (e.g., text from signs and/orroad markings). System 100 may then use the text information as part ofor as the basis of determining whether vehicle 200 is within a turnlane. For example, system 100 may identify certain words indicative of aturn lane (e.g., “turn, “right,” left,” etc.).

Determining the distance s1 or s2 may be based on direct measurements ofdistance, such as via position sensor 130, or may be based on analysisof captured image data by image processor 190 and may be used inconnection with map data. The distance may be measured from any portionof the interior or exterior vehicle 200 or 200 e, including but notlimited to the front of vehicle 200 or 200 e, a portion of vehicle 200or 200 e such as a headlight or front license plate, a positionas-installed of image capture devices 122-126, a determined centroid ofvehicle 200 or 200 e, the rear of vehicle 200 or 200 e, one or morewindshields or mirrors associated with vehicle 200 or 200 e, wheels ofvehicle 200 or 200 e, right or left sides or windows of vehicle 200 or200 e, a point associated with the roof of vehicle 200 or 200 e, or apoint associated with the chassis of vehicle 200 or 200 e.

In some embodiments, determining the distance from vehicle 200 or 200 eto traffic light fixture 1006 may be sufficient to assist processingunit 110 in calculating a braking distance or other such measurement. Inother embodiments, however, traffic light fixture 1006 may be locatedpast the point at which vehicle 200 would be required to stop. In theseembodiments, the distance to the intersection may be determined by usingone or more of warning lines 1010 or stop line 1012.

As discussed above in association with FIG. 10, these distances may becalculated using an estimation process employing expansion and scaling.At an intersection 1002 with two traffic light fixtures, for example,system 100 may measure the relative distance between the two fixturesover a period of time as captured in the image data, and then use thatrelative distance to estimate the distance to stop line 1012. In someembodiments, these measurements may be repeated over time for increasedprecision. Precision may also be increased by other methods. Forexample, if three or four traffic light fixtures are situated atintersection 1002, relative distances may be calculated between each ofthe fixtures and averaged. Additionally or alternatively, the distancemay be estimated using a Kalman filter, as discussed above.

System 100 may still need additional information or inputs to determinethe location of a stop line 1012 relative to an intersection 1002 and/ortraffic light fixtures 1006. In some embodiments, system 100 may usepreviously-stored map data gathered from prior trips to theintersection. This information may be received from traffic lightdetection module 810, or image analysis module 805. A distance measure Zmay also be derived from the image data using the equation

Z=fW/w

where W is the known distance between two traffic light fixtures 1006, wis the distance in the image data in pixels, and f is the focal lengthof the particular image capture device 122-126 in pixels.

In FIG. 11, an inset 1030 shows the two statuses of traffic lightfixture 1006 relevant to the example. Based on image data from at leastone image received via data interface 128 from image capture devices122-126, system 100 may be configured to determine the status of trafficlight fixture 1006. Upon approaching intersection 1002, system 100 mayfirst determine that traffic light fixture 1006 is displaying a solidred light, as seen in image 1 of inset 1030. In the example of FIG. 11,lane 1011 is a left turn lane, so unless roadway 1000 is located in ajurisdiction where left turns on red are legal, vehicle 200 must stop atstop line 1012 and remain stationary until traffic light fixture 1006 isdetermined to have changed.

In alternative embodiments (not shown), vehicle 200 may be traveling ina lane that is determined to be a right turn lane, and assuming thatright terms on red are legally permissible in that jurisdiction, system100 may cause a system response enabling vehicle 200 to turn right whiletraffic light fixture 1006 remains red. Lane 1001 may be a straightahead only lane. When the traffic light at traffic light fixture 1006 isgreen, vehicle 200 e in lane 1001 may proceed to pass intersection 1002.When the traffic light at traffic light fixture 1006 is yellow, vehicle200 e in lane 1001 may brake to reduce the speed or to stop at stop line1012, or accelerate to speed up and pass intersection 1002. When thetraffic light at traffic light fixture 1006 is red, vehicle 200 e muststop before stop line 1012.

Subsequently, as seen in image 2 of inset 1030, traffic light fixture1006 may shift from a solid red light to a solid green. In someembodiments, the solid green light may also include an arrow indicatinga permitted left turn. System 100 may be configured to detect not onlythat the color of the light has changed, but also that an arrow hasappeared within the traffic light based on pixel-level image analysis byimage processor 190 of acquired images of traffic light fixture 1006. Insome embodiments, in order for system 100 to detect that the color of atraffic light has changed, system 100 may analyze captured images over aperiod of time (e.g., 1 second, 5 seconds, 10 seconds, 15, seconds, 20seconds, 30 seconds, 1 minute, etc.) in order to recognize the statuschange (or transition from one color to another) of the traffic light.System 100 may further determine once traffic light fixture 1006 changesto display the green arrow whether it is safe or authorized to proceedin the direction indicated by the status of the light.

Although not shown, in the example of FIG. 11, as vehicle 200 or 200 eapproaches intersection 1002, traffic light fixture 1006 may display aflashing or blinking yellow light. As may be familiar to one of ordinaryskill in the relevant art, some jurisdictions use blinking yellow lightsto essentially serve the same functions as “Yield” or “Stop” signsduring certain times of the day, while still permitting the lanes oftravel to be fully stopped with a red light or fully authorized toproceed with a green light.

As outlined above, system 100 may determine that traffic light fixture1006 is displaying a blinking yellow light. As described above, trafficlight fixture 1006 may regulate both lanes 1001 and 1011. As one ofordinary skill would understand, a blinking yellow light authorizes anapproaching vehicle to turn or proceed straight ahead at will, but onlyif it is safe to do so. Drivers are expected to use caution at theintersection and determine that no potential collisions or other hazardswould result from making the turn too early. Thus, system 100 mayprovide a warning or alert to a driver of vehicle 200 e (regardless ofwhether vehicle 200 e is a conventional vehicle operated by a humandriver, or when vehicle 200 e is an autonomous vehicle that can becontrolled by a human operator). Additionally or alternatively, system100 may cause vehicle 200 e to take a navigational response, such asacceleration, deceleration (e.g., via braking), maintaining the currentspeed, etc.

One such determination that system 100 may make is whether one or morevehicles are crossing the intersection. For example, while travel lanestraveling in the south-north direction (i.e., the direction in whichvehicle 200 e is travelling) of a roadway may have a blinking yellowlight in an “ahead” lane (in which a vehicle can drive forward ahead),traffic traveling the east-west direction (i.e., the left and rightdirection of intersection 1002 in the view shown in FIG. 11) may have agreen light to proceed straight through the intersection. Accordingly, acollision could occur if the operator of the vehicle with the blinkingyellow signal fails to yield to a vehicle that is also crossing theintersection in the east-west direction. If system 100 detects thepresence of another vehicle in the intersection or is about to enter theintersection in the east-west direction, via image data captured fromone or more of image capture devices 122-126, system 100 may issue awarning or alert to the driver of vehicle 200 e, or cause braking system230 to brake to reduce the speed of vehicle 200 e or even to stop beforeentering the intersection.

System 100 may provide a traffic light warning function configured toalert the driver when the vehicle is about to enter a junction orintersection with a red traffic light indication, or with an indicationof changing status from green to red (e.g., a yellow light). Forexample, system 100 may determine which traffic light is relevant to thevehicle. In some embodiments, system 100 may also determine the severityof the driver's unawareness or awareness to it (e.g., no braking hasbeen taken by the driver within a certain distance to the traffic lightor intersection). In cases in which system 100 determines that thedriver is not paying attention to the need to stop, system 100 may issuea red traffic light alert to the driver, which may be an audio alert, avisual alert, a tactile alert, or a combination thereof. In someembodiments, system 100 may automatically cause a system response, suchas a navigational response by the vehicle, in response to detecting ared traffic light ahead of the vehicle or a transition of the trafficlight from green to yellow to red.

In some embodiments, system 100 may determine whether to cause a systemresponse in the vehicle based on at least one of the relevancy of thetraffic light, the status of the relevant traffic light, estimate of adistance from the vehicle to a traffic light fixture or a stop line(e.g., s1 and/or s2 in FIG. 11). System 100 may also determine whetherone or more alert suppression schemes are satisfied to avoid falsealarms.

Traffic lights may be detected from images captured by one or more imagecapture devices 122-126. As discussed above, traffic light detectionmodule 810 shown in FIG. 8 may store instructions executable byprocessing unit 110 to detect traffic lights and/or traffic lightfixtures, including the status of traffic lights (e.g., color, location,etc.). In some embodiments, processing unit 110 may analyze one or moreimages to locate and classify traffic lights candidates appearing in theimages. Using any one or more of the traffic light detection methodsdescribed above, processing unit 110 may identify a bounding rectangleand the color of a traffic light in the image, thereby identifying thetraffic light and its status. In some embodiments, processing unit 110may identify a transition status (e.g., from green to yellow to red)based on the colors of the traffic light captured in a plurality ofimages (e.g., two or more sequentially acquired images).

After traffic lights are detected from the images, processing unit 110may execute instructions stored in distance determination module 815 tomake one or more measurements related to the traffic lights from theimages. For example, processing unit 110 may measure a distance in thephysical world from the vehicle to the stop line, the traffic light, orthe traffic light fixture to which the traffic light is attached. Insome embodiments, processing unit 110 may also measure the lateraldistance of the vehicle to a traffic light located on one side of theroad. Processing unit 110 may measure the traffic light height measuredfrom the ground. Processing unit 110 may measure the size of the trafficlight, such as the height and width of the traffic light.

Processing unit 110 may estimate the distance from the vehicle to thejunction (or intersection). In one embodiment, processing unit 110 maydetect a stop line at the intersection in the images, and may measurethe distance from the vehicle to the stop line (or junction where thestop line is located) in the physical world. The measured distance maybe regarded as the distance from the vehicle to the junction (orintersection). In some embodiments, through distance estimation module815, processing unit 110 may estimate the distance to the junction basedon one or more of the traffic lights in the images detected by trafficlight detection module 810. In some embodiments, the distance from thevehicle to the traffic lights or traffic light fixtures may bedetermined based on both the stop line detected in the images and thetraffic lights detected in the images.

Since the traffic lights arrangement in the junction may be regiondependent, distance estimation module 815 may take into account theregion (or location) in which the vehicle is being operated. Forexample, different algorithms may be executed for United States andEurope.

When determining the distance from the vehicle to the traffic lights (ortraffic light fixtures or intersection), processing unit 110 may attemptto cluster the traffic lights detected from the images into two clusters(or rows). If two clusters of traffic lights are detected, processingunit 110 may measure the length of the junction (or intersection). Forexample, the length of the junction may be measured based on the twoclusters of traffic lights (the far and close traffic lights). In someembodiments, the length of the junction may be measured based on thedistance between the two clusters of traffic lights in the physicalworld.

Processing unit 110 may measure distances from the vehicle to one ormore of the traffic lights or the distance between the two clusters oftraffic lights using any methods known in the art based on one or moreimages. The distance from the vehicle to the junction may be measured as(the average distance from the vehicle to all of the trafficlights)−(length of the junction). In some embodiments, when a stop lineis detected, the distance from the vehicle to the junction measuredbased on the stop line may be averaged with the distance from thevehicle to the junction measured based on the traffic lights. Forexample, if the distance from the vehicle to the junction measured basedon the stop line is D1, and the distance from the vehicle to thejunction measured based on the traffic lights is D2, the overalldistance from the vehicle to the junction may be calculated asD0=D1*w1+D2*w2, where w1 and w2 are weights. In some embodiments, weightw1 may be greater than weight w2.

In some embodiment, when a single cluster of traffic lights is detected(e.g., a single row or line of traffic lights), processing unit 110 maydetermine the traffic light location by a region code. For example, afirst region code may designate United States, while a second regioncode may designate Europe. The region codes may be stored in memory 140or 150. There may be other region codes for other countries. Forexample, based on the region code, a predetermined (or default) lengthof junction may be used. The length of the junction (or intersection)means the span of the junction (or intersection) in the same directionas the driving direction of the vehicle. For example, when the vehicleis passing the junction (or intersection) in the north-south direction(rather than the east-west direction), the length of the junction is thespan of the junction in the north-south direction. As another example,based on the region code, a default location of the traffic lights maybe assumed (e.g., located at far end of intersection or located at closeend of intersection). Processing unit 110 may measure the distance fromthe vehicle to the junction based on all traffic lights, such as thedistances from all traffic lights to the vehicle. In some embodiments,the measurements of the distances (e.g., the distances from the vehicleto the stop line, to the traffic lights, etc.) may be averaged andfiltered using, for example, a predict-correct scheme by vehicle egomotion.

When the vehicle is operated in the United States, some traffic lightsmay not be located near the stopping location (e.g., stop line in frontof the junction). In some cases, one or more of the traffic lights atthe junction are located on the far side of the junction. Processingunit 110 may first attempt to cluster the traffic lights detected in theimages into two clusters or rows (a close cluster and a far cluster). Ifthe detected traffic lights can be clustered into two clusters,processing unit 110 may calculate the length of junction, e.g., based onthe distance between the two clusters of traffic lights. If theclustering fails (i.e., traffic lights cannot be clustered into twoclusters), all of the traffic lights may be assumed to be located at thefar side of the junction. In the case of one single cluster, the lengthof junction may use a predetermined length of 20 meters (or any othersuitable length). Distance from the vehicle to the junction is measuredbased on the average of all of the distances from all of the trafficlights to the vehicle, and the length of the junction, e.g., (theaverage of all of the distances from all of the traffic lights to thevehicle)−(length of the junction).

When the vehicle is operated in Europe, the traffic lights may typicallybe located at the stopping location of the vehicle (e.g., near the stopline). Processing unit 110 may attempt to cluster the detected trafficlights into two clusters (e.g., close and far clusters). In case ofclustering success, processing unit 110 may calculate the length of thejunction, e.g., by the distance between the two clusters of trafficlights. In case of clustering failure (e.g., only one cluster isdetected), all of the traffic lights may be assumed to be located at theclose side of the junction. The distance from the vehicle to thejunction may be measured based on the average of the distances from allof the traffic lights to the vehicle and the length of the junction,e.g., (the average of all of the distances from all of the trafficlights to the vehicle)−(length of the junction). If a stop line is alsodetected in the images, the same average discussed above using weightsw1 and w2 may be performed, i.e., D0=D1*w 1+D2*w2, where D1 is thedistance measured based on the detected stop line, and D2 is thedistance measured based on the clustering of traffic lights. However, inthe case of Europe, the weight w1 may be smaller than the weight w1 usedin the case of United States.

Through traffic light detection module 810, processing unit 110 maydetermine the relevancy of a traffic light to the vehicle. For exampleprocessing unit 110 may find the most relevant traffic light in thescene from all of the traffic lights captured in the images using one ormore of the methods described above. Processing unit 110 may useinformation from one or more of the traffic lights and information fromthe at least a portion of a scene captured in the images. For example,an input into an algorithm for determining the relevancy of a trafficlight may include traffic light 3D location (x, y, z), road information,and Spot's super-resolution information (e.g., for arrow, circularshapes related to the traffic light). An output of the algorithm may bea relevancy score for each traffic light. The one with the highest scoremay be the most relevant traffic light.

Through traffic light detection module 810, processing unit 110 maydetermine various features for each traffic light detected in theimages. For example, processing unit 110 may determine the distance fromthe center of a road model to the traffic light, a super resolutionscore (e.g., for arrow and/or circular shapes), age, and location in thereal world. In some embodiments, processing unit 110 may classify thefeatures using an internal module for a relevancy score. The trafficlight with the highest relevancy score may be marked as the mostrelevant traffic light for the vehicle. Traffic lights with highdistance from the junction (as calculated by distance determinationmodule 815) may be automatically filtered and treated as irrelevant(e.g., being assigned low relevancy scores).

In some embodiments, the relevancy score may be determined based on theangle of the traffic light relative to the lane in which vehicle 200 istraveling. When the angle is within a predetermined range of angles(e.g., −15 degrees to 15 degrees), the score may be assigned with a highvalue; otherwise, the score may be assigned with a low value. Otherscoring schemes may also be used. For example, processing unit 110 mayselect one of the traffic lights that best fits the lane in which thevehicle is travelling based on the smallest angle associated with thetraffic lights. In some embodiments, prior information regarding thejunction layout and the layout of the traffic lights in that junctionmay be stored in a memory, and that information may be used to match thevisual information obtain in the captured images of the traffic lightsand/or junction. A matching score for the different traffic lightsincluded in the captured images may be calculated. The traffic lighthaving the highest matching score may be deemed as most relevant to thelane in which the vehicle is travelling.

System 100 may cause a system response in the vehicle when one or moreconditions or criteria are satisfied based on an assessment ofinformation derived from the captured images. For example, processingunit 110 may determine whether the vehicle is approaching a red relevanttraffic light. Processing unit 110 may determine whether a traffic lightis detected from the captured images and determine the status of thetraffic light. For example, based on analysis of one or more capturedimages including the traffic lights, processing unit 110 may determinewhether the detected traffic light is red, yellow, or is transitioningfrom green to yellow (and soon will be transitioning to red). Whenprocessing unit 110 determines that one or more conditions aresatisfied, and the vehicle is approaching a red light (a yellow light,or a green light that is transitioning to a yellow light), processingunit 110 may cause a system response. The system response may include analert or warning notification to the driver of the vehicle. In someembodiments, the system response may include an acceleration (to passthe intersection more quickly), a braking (to reduce the speed or tostop before the intersection), or maintaining the current speed (tosafely pass the intersection).

The one or more conditions or criteria for triggering a system responsemay include a TTC based condition, a minimum deceleration basedcondition, or both. To trigger a system response based on the TTCcondition, processing unit 110 may determine, for each time frame (e.g.,9 fps), the current TTC for the vehicle (e.g., amount of time for thevehicle travel from the current position to the stop line orintersection). The TTC may be calculated based on the current distanceto the junction, the speed of the vehicle, and/or the acceleration ofthe vehicle. The TTC may be compared with a predetermined timethreshold. If the TTC is smaller than or equal to the predetermined timethreshold, a system response may be triggered. The predetermined timethreshold may be 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds,or any other suitable time.

To trigger the system response based on the minimum decelerationcondition, processing unit 110 may calculate, in each time frame, theminimum deceleration of the vehicle required for the vehicle to fullystop at the junction within the measured distance from the vehicle tothe junction. The required minimum deceleration may be calculated basedon the current distance Z from the vehicle to the junction and thecurrent speed V of the vehicle as: dec=V²/2Z (m/sec²). Processing unit110 may compare the required minimum deceleration with a predetermineddeceleration threshold. If the required minimum deceleration is greaterthan or equal to the predetermined deceleration threshold, a systemresponse may be triggered.

In some embodiments, the system response may be triggered when both TTCbased condition and the minimum deceleration based condition aresatisfied. For example, processing unit 110 may determine to cause asystem response when the current TTC for the vehicle is smaller than orequal to the predetermined time threshold, and the required minimumdeceleration is greater than or equal to the predetermined decelerationthreshold.

In some embodiments, when one or both of the above conditions aresatisfied and when the vehicle is approaching a red traffic light (or ayellow traffic light, or a green traffic light that is transitioning toyellow, which will soon transition to red), processing unit 110 may notcause a system response if one or more suppression schemes aresatisfied. The suppression schemes may be implemented in suppressionmodule 825. Suppression schemes may be based on vision informationand/or based on driver behavior.

Suppression schemes based on vision information may include suppressinga system response when detected traffic lights are on the far left sideor far right side. In such situations, the traffic lights are consideredas regulating other roads. Suppression schemes based on visioninformation may also include suppressing a system response when thereare some traffic lights in the junction that has conflict in theircolors to the relevant traffic light for the vehicle. Suppressionschemes based on vision information may also include suppressing asystem response when only one traffic light is detected. Suppressionschemes based on vision information may also include suppressing asystem response when the junction of the detected traffic light is morethan a predetermined distance away, such as 60 meters away, 80 metersaway, or any other suitable distance away. Suppression schemes based onvision information may also include suppressing a system response whenthe distance to a preceding vehicle is less than a predetermineddistance, such as 40 meters, 35 meters, 50 meters, or any other suitabledistance. The term “preceding vehicle” refers to a vehicle that isdriving in the same direction in front of the vehicle on which system100 is provided (e.g., vehicle 200). A preceding vehicle may be detectedby its tail lights.

Suppression schemes based on the driver behavior may include suppressinga system response (e.g., an alert or warning notification to the driverof a vehicle) when a signal from braking system 230 indicates that thedriver has pressed the brakes. This may indicate that the driver hasfull control of the vehicle and is probably aware of the traffic lightsahead of the vehicle. Suppression schemes based on the driver behaviormay also include suppressing a system response when a signal from a turnlight (e.g., left turn light or right turn light) indicates that thedriver has turned on the left or right turn light. This, when coupledwith detection of an arrow in the traffic light (e.g., a left turn arrowor a right turn arrow), or a turn lane marking on the road surface inthe images, may indicate that the driver is observing the traffic light.Suppression schemes based on the driver behavior may include suppressinga system response when a signal from steering system 240 indicates thatthe driver is making a sharp left or right turn.

FIG. 12 is a flowchart illustrating a method of detecting a trafficlight consistent with the disclosed embodiments. Method 1200 may beexecuted by processing unit 110. Method 1200 may include receiving, fromat least one image capture device, a plurality of images representativeof an area forward of the vehicle (step 1205). For example, processingunit 110 may receive at least one image (e.g., one or more images)captured by at least one of image capture devices 122-126. Method 1200may also include analyzing at least one of the plurality of images todetermine a status of the at least one traffic light (step 1210). Forexample, processing unit 110, via traffic light detection module 810,may detect one or more traffic lights in the captured images, and mayfurther detect the status of a traffic light based on the capturedimages. The status may include a green light, a yellow light, a redlight, a yellow and blinking light, etc. The status may also include atransition from green to yellow, or from yellow to red, or from red togreen. The status may further include an arrow in a traffic lightindicating a turn is allowed.

Method 1200 may also include determining an estimated amount of timeuntil the vehicle will reach an intersection associated with the trafficlight fixture (step 1215). The estimated amount of time may also bereferred to as the time to contact (TTC). For example, processing unit110, via time determination module 820, may determine the TTC fromvehicle's current position to the intersection, e.g., to the stop lineof the intersection, if the stop line is visible in the images, or to astarting location of the intersection where the stop line or apedestrian line is supposed to be located.

Method 1200 may further include causing a system response based on thestatus of at least one traffic light and the estimated amount of timeuntil the vehicle will reach the intersection (step 1220). For example,processing unit 110 may cause the system response when the traffic lightis yellow or red, or when the traffic light is transitioning from yellowto red, or when the traffic light is transitioning from green to yellow.Processing unit 110 may cause the system response when the status of thetraffic light is one of the above situations, and the TTC is smallerthan or equal to a predetermined time threshold. For example, processingunit 110 may cause vehicle to brake such that the vehicle can fully stopbefore reaching the intersection (e.g., before reaching the stop line orthe pedestrian crossing line at the starting location of theintersection).

Alternatively or additionally, processing unit 110 may cause an alert orwarning notification to be displayed or sounded to the driver of thevehicle. As another example, when the traffic light is yellow and whenthe TTC is greater than the predetermined time threshold, processingunit 110 may determine that if the vehicle maintains the current speed,the vehicle may safely pass the intersection without causing anemergency braking. Thus, processing unit 110 may cause the vehicle(e.g., via throttling system 220) to maintain the current speed and passthe intersection at the current speed. Alternatively or additionally,processing unit 110 may determine that if the vehicle accelerates andcontinues driving with a greater speed (higher than the current speed),the vehicle will safely pass the intersection despite a current yellowlight, processing unit 110 may cause the vehicle to accelerate to passthe intersection more quickly.

Method 1200 may include other steps. For example, processing unit 110may determine whether the at least one traffic light is relevant to thevehicle. Processing unit 110 may suppress the system response when theat least one traffic light is determined to be not relevant to thevehicle. Processing unit 110 may analyze at least one of the pluralityof images to identify a marking on a road in the area forward of thevehicle. Processing unit 110 may determine whether the at least onetraffic light is relevant to the vehicle based on the marking on theroad. The marking on the road may include a turn lane arrow, and the atleast one processing device is further programmed to suppress the systemresponse based on detecting the turn lane arrow.

In some embodiments, processing unit 110 may determine the amount oftime until the vehicle will reach the intersection based on a distanceof the vehicle to a stop line associated with the traffic light fixture.The distance of the vehicle to the stop line may be determined based onanalysis of at least two of the plurality of images including the stopline. Processing unit 110 may determine the amount of time until thevehicle will reach the intersection based on a distance of the vehicleto the traffic light fixture. The distance of the vehicle to the trafficlight fixture may be determined based on analysis of at least two of theplurality of images including the traffic light fixture. Processing unit110 may suppress the system response when a signal from a steeringsystem of the vehicle indicates that the vehicle turns left or right.The system response may include applying vehicle brakes. Processing unit110 may provide a visual or auditory notice to a driver of the vehicleprior to causing the system response.

In some embodiments, processing unit 110 may cause the system responsewhen the status of the at least one traffic light is represented by ayellow or red color, and when a minimum deceleration needed for thevehicle to travel a distance from a current position of the vehicle tothe intersection to achieve a full stop before reaching the intersectionis equal to or greater than a predetermined deceleration threshold.Processing unit 110 may determine the minimum deceleration based on acurrent speed of the vehicle and the distance from the current positionof the vehicle to the intersection.

In some embodiments, processing unit 110 may cause the system responsewhen the status of the at least one traffic light is represented by ayellow or red color, and when the estimated amount of time for thevehicle to travel a distance from a current position of the vehicle tothe intersection to achieve a full stop before reaching the intersectionis equal to or greater than a predetermined deceleration threshold.

In some embodiments, processing unit 110 may cause the system responsewhen the status of at least one traffic light is represented by a yellowor red color, when the estimated amount of time until the vehicle willreach the intersection is equal to or less than a predetermined timethreshold, and when one or more predetermined suppression schemes arenot satisfied.

In some embodiments, processing unit 110 may cause the system responsewhen the status of the at least one traffic light is represented by ayellow or red color, when a minimum deceleration needed for the vehicleto travel a distance from a current position of the vehicle to theintersection to achieve a full stop before reaching the intersection isequal to or greater than a predetermined deceleration threshold, andwhen one or more predetermined suppression schemes are not satisfied.

In some embodiments, the one or more predetermined suppression schemesmay include suppressing the system response when at least one of thefollowing is satisfied: the at least one traffic light is on a left orright side of the field of view at a distance to a center of the fieldof view that is greater than a predetermined distance, or the trafficlight fixture including the at least one traffic light is more than apredetermined distance away from the vehicle, or a distance from thevehicle to a preceding vehicle is less than a predetermined distance.

In some embodiments, the one or more predetermined suppression schemesmay include suppressing the system response when at least one of thefollowing is satisfied: a signal from a braking system of the vehicleindicates that the driver has applied a brake, or a signal from alighting system of the vehicle indicates that a left or right turnsignal is blinking and the status of the at least one traffic light isrepresented by a green color with an arrow indication, or a signal froma steering system indicates that the vehicle is making a sharp turn.

FIG. 13 is a flowchart illustrating another method of detecting atraffic light, consistent with another embodiment of the presentdisclosure. Method 1300 may include receiving, from at least one imagecapture device, a plurality of images representative of an area forwardof the vehicle (step 1305). Step 1305 may be similar to step 1205. Thus,the discussion of step 1205 is also applicable to step 1305. Method 1300may also include analyzing at least one of the plurality of images todetermine a status of the at least one traffic light (step 1310). Step1310 may be similar to step 1210. Thus, the discussion of step 1210 isalso applicable to step 1310.

Method 1300 may also include determining a distance from the vehicle toan intersection (step 1315). The distance from the vehicle to theintersection may be determined based on at least one of a distance to astop line or a pedestrian line at the starting location of theintersection as detected in the captured images, and a distance from thevehicle to at least one of the traffic light detected in the capturedimages. In one embodiment, the distance to the intersection from thevehicle may be treated as the distance to the stop line captured in theimages, and may be determined based on the analysis of the capturedimages including the stop line. In one embodiment, the distance to theintersection from the vehicle may be the distance to a starting locationwhere the stop line is supposed to be located, and the distance to theintersection may be determined based on one or more distances from thevehicle to one or more traffic lights detected in the images. In someembodiments, a default length of junction may be used in the calculationof the distance to the intersection.

Method 1300 may further include causing a system response based on thestatus of at least one traffic light and the distance from the vehicleto the intersection (step 1320). For example, processing unit 110 maycause a system response, such as an alert to the driver, or braking ofthe vehicle, when the traffic light is yellow or red, and the distanceto the intersection is smaller than or equal to a predetermineddistance, such that a time to contact (TTC) from the current position ofthe vehicle to the intersection is smaller than or equal to apredetermined time threshold.

As another example, processing unit 110 may calculate a minimumdeceleration required for the vehicle to achieve a full stop beforereaching the starting location (e.g., the stop line) of theintersection. Processing unit 110 may cause a system response such asbraking of the vehicle when the minimum deceleration required is greaterthan or equal to a predetermined deceleration threshold. For example,when the minimum deceleration calculated based on the distance to theintersection and the current speed of the vehicle is greater than 10m/s² (an absolute value of the deceleration), where unit m/s² ismeter/second/second, processing unit 110 may determine that the minimumdeceleration is too large and the driver or the vehicle must brake nowin order to safely stop at the stop line (or the starting location ofthe intersection). Thus, processing unit 110 may cause the vehicle orthe driver to brake.

Method 1300 may include other steps. For example, processing unit 110may determine an amount of time until the vehicle will reach anintersection associated with the traffic light fixture based on thedistance and a current speed of the vehicle, compare the amount of timeto a predetermine time threshold, and trigger the system response whenthe amount of time is smaller than or equal to the predetermined timethreshold.

In some embodiments, processing unit 110 may determine an amount of timeuntil the vehicle will reach an intersection associated with the trafficlight fixture based on the distance and a current speed of the vehicle,compare the amount of time to a predetermine time threshold, determinewhether a predetermined suppression scheme is satisfied, and trigger thesystem response when the amount of time is smaller than or equal to thepredetermined time threshold and when the predetermined suppressionscheme is not satisfied.

In some embodiments, processing unit 110 may determine, based on thedistance from the vehicle to the intersection, a minimum decelerationrequired for vehicle to stop before the intersection within the distancefrom the vehicle to the intersection, compare the minimum decelerationrequired with a predetermined deceleration threshold, and trigger thesystem response when the minimum deceleration required is greater thanor equal to the predetermined deceleration threshold.

In some embodiments, processing unit 110 may determine, based on thedistance from the vehicle to the intersection, a minimum decelerationrequired for vehicle to stop before the intersection within the distancefrom the vehicle to the intersection, compare the minimum decelerationrequired with a predetermined deceleration threshold, determine whethera predetermined suppression scheme is satisfied, and trigger the systemresponse when the minimum deceleration required is greater than or equalto the predetermined deceleration threshold and when the predeterminedsuppression scheme is not satisfied.

In some embodiments, processing unit 110 may determine an amount of timeuntil the vehicle will reach an intersection associated with the trafficlight fixture based on the distance and a current speed of the vehicle,compare the amount of time to a predetermine time threshold, determine,based on the distance from the vehicle to the intersection, a minimumdeceleration required for vehicle to stop before the intersection withinthe distance from the vehicle to the intersection, compare the minimumdeceleration required with a predetermined deceleration threshold, andtrigger the system response when the amount of time is smaller than orequal to the predetermined time threshold and when the minimumdeceleration required is greater than or equal to the predetermineddeceleration threshold.

In some embodiments, processing unit 110 may determine an amount of timeuntil the vehicle will reach an intersection associated with the trafficlight fixture based on the distance and a current speed of the vehicle,compare the amount of time to a predetermine time threshold, determine,based on the distance from the vehicle to the intersection, a minimumdeceleration required for vehicle to stop before the intersection withinthe distance from the vehicle to the intersection, compare the minimumdeceleration required with a predetermined deceleration threshold,determine whether a predetermined suppression scheme is satisfied, andtrigger the system response when the amount of time is smaller than orequal to the predetermined time threshold, when the minimum decelerationrequired is greater than or equal to the predetermined decelerationthreshold, and when the predetermined suppression scheme is notsatisfied.

Examples of the present disclosure relate to a traffic light, detectionthereof and navigational responses based on a state of the trafficlight. However, it would be appreciated that examples of the presentlydisclosed subject matter can be applied to any visually detectable(e.g., by a camera mounted onboard a vehicle) traffic control object,whose state changes dynamically, and a visual appearance of the controlobject respectively changes to indicate the change in the state of theobject. By way of example, the change can involve a change in a color ofthe object, the part of the object which emits light, the shape that isdisplayed by the object, etc. In another example, the visual appearancecan also indicate the relevance of the control object. For example, ifan arrow appears on the object, it may be intended for vehicles thatnavigate in the direction indicated by the arrow. As another example,several control objects can be adjacent to one another, and the locationof a control object, in particular relative to one or more other controlobjects (typically similar to that control object) can indicate itsrelevance (or irrelevance) to a vehicle that is navigating to a certaindestination (or in a certain direction).

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1-31. (canceled)
 32. A traffic light detection system for installationin a vehicle, the system comprising: a processing unit comprising acentral processing unit (CPU) and support circuitry, the processing unitprogrammed to: access images representative of an area in a direction oftravel of the vehicle, the images captured by an image acquisition unit,the area including a plurality of traffic light fixtures; analyze theimages to identify the plurality of candidate traffic light fixtures;select a traffic light fixture from the plurality of candidate trafficlight fixtures based on the position of the vehicle; determine a statusof the traffic light fixture; and cause a navigational response of thevehicle based on the status of the traffic light fixture and a distancefrom the vehicle to an intersection controlled by the traffic lightfixture.
 33. The system of claim 32, wherein selecting the traffic lightfixture from the plurality of candidate traffic light fixtures based onthe position of the vehicle further includes determining whether thetraffic light fixture is relevant to the vehicle.
 34. The system ofclaim 32, wherein the navigational response comprises at least one of: aturn, a lane shift, a change in acceleration, a change in velocity, orbraking.
 35. The system of claim 32, wherein the at least one processingdevice is further programmed to cause the navigational response when thestatus of the traffic light fixture is represented by a yellow or redcolor.
 36. The system of claim 32, wherein the at least one processingdevice is further programmed to cause the navigational response when anestimated amount of time until the vehicle will reach the intersectionis equal to or less than a predetermined time threshold.
 37. The systemof claim 32, wherein the at least one processing device is furtherprogrammed to cause the navigational response when the status of thetraffic light fixture is represented by a yellow or red color, and anestimated amount of time until the vehicle will reach the intersectionis equal to or less than a predetermined time threshold.
 38. The systemof claim 32, wherein the at least one processing device is furtherprogrammed to analyze at least one of the of images to identify amarking on a road in the area forward of the vehicle.
 39. A method fortraffic light detection, the method comprising: accessing imagesrepresentative of an area in a direction of travel of a vehicle, theimages captured by an image acquisition unit, the area including aplurality of traffic light fixtures; analyzing the images to identifythe plurality of candidate traffic light fixtures; selecting a trafficlight fixture from the plurality of candidate traffic light fixturesbased on the position of the vehicle; determining a status of thetraffic light fixture; and causing a navigational response of thevehicle based on the status of the traffic light fixture and a distancefrom the vehicle to an intersection controlled by the traffic lightfixture.
 40. The method of claim 39, wherein selecting the traffic lightfixture from the plurality of candidate traffic light fixtures based onthe position of the vehicle further includes determining whether thetraffic light fixture is relevant to the vehicle.
 41. The method ofclaim 39, wherein the navigational response comprises at least one of: aturn, a lane shift, a change in acceleration, a change in velocity, orbraking.
 42. The method of claim 39, further comprising causing thenavigational response when the status of the traffic light fixture isrepresented by a yellow or red color.
 43. The method of claim 39,further comprising causing the navigational response when an estimatedamount of time until the vehicle will reach the intersection is equal toor less than a predetermined time threshold.
 44. The method of claim 39,further comprising causing the navigational response when the status ofthe traffic light fixture is represented by a yellow or red color, andan estimated amount of time until the vehicle will reach theintersection is equal to or less than a predetermined time threshold.45. The method of claim 39, further comprising analyzing at least one ofthe images to identify a marking on a road in the area forward of thevehicle.
 46. A non-transitory computer readable medium comprisingexecutable instructions that when executed by at least one processingdevice cause the at least one processing device to detect a trafficlight and perform operations comprising: accessing images representativeof an area in a direction of travel of a vehicle, the images captured byan image acquisition unit, the area including a plurality of trafficlight fixtures; analyzing the images to identify the plurality ofcandidate traffic light fixtures; selecting a traffic light fixture fromthe plurality of candidate traffic light fixtures based on the positionof the vehicle; determining a status of the traffic light fixture; andcausing a navigational response of the vehicle based on the status ofthe traffic light fixture and a distance from the vehicle to anintersection controlled by the traffic light fixture.
 47. Thenon-transitory computer readable medium of claim 46, wherein selectingthe traffic light fixture from the plurality of candidate traffic lightfixtures based on the position of the vehicle further includesdetermining whether the traffic light fixture is relevant to thevehicle.
 48. The non-transitory computer readable medium of claim 46,wherein the navigational response comprises at least one of: a turn, alane shift, a change in acceleration, a change in velocity, or braking.49. The non-transitory computer readable medium of claim 46, furthercomprising causing the navigational response when the status of thetraffic light fixture is represented by a yellow or red color.
 50. Thenon-transitory computer readable medium of claim 46, further comprisingcausing the navigational response when an estimated amount of time untilthe vehicle will reach the intersection is equal to or less than apredetermined time threshold.
 51. The non-transitory computer readablemedium of claim 46, further comprising causing the navigational responsewhen the status of the traffic light fixture is represented by a yellowor red color, and an estimated amount of time until the vehicle willreach the intersection is equal to or less than a predetermined timethreshold.